Definitions drift
Tables, schemas and resources are copied between tools, making development and change harder to coordinate.
01 / SCALE GAPDecision guide · 01
Your data stack, connected.Your team, working with AI.
Bring metadata, AI-assisted development, streaming pipelines and lakehouse operations into one workspace, with enterprise engineering and support.
The inflection point
The data stack needs a shared workflow.
A working lakehouse spans source systems, metadata, processing, storage and analytics. Realfuture connects these parts so teams can develop, investigate and operate with shared context.
Operations fragment
Pipeline definitions, target tables and runtime evidence remain split across tools.
02 / SCALE GAPAI lacks context
A prompt alone cannot explain your schemas, dependencies or the state of a failing job.
03 / SCALE GAPOperating model comparison
Connect the work across your data stack.
Compare the work your team assembles internally with the workflows Realfuture provides. Component capabilities and support remain version-specific.
ResponsibilityOpen-source assemblyWith Realfuture
Development & AITeam-specific scripts and conventionsCatalog-aware SQL, CDC and ETL, AI assistance, validation and publishing
Catalogs & lakehouseSeparate metadata, pipeline and table workflowsShared catalogs and integrations for Paimon, Fluss, Doris and StarRocks
Production recoveryRunbooks assembled and maintained internallyOperational context—including recovery, observability, and history—stays with each job
GovernanceAssembled separately for each projectWorkspace permissions, operation records and configured AI access
Commercial ownershipCommunity support plus internal expertiseEnterprise support with accountable delivery
PortabilityPossible, but operationally expensiveConsistent workflow across infrastructure choices
Commercial value
Value that compounds with every workload.
A shared operating model lowers the effort to add each workload, environment, and team.
Ship sooner
Reuse table definitions and develop with AI, validation and previews.
VALUE / TIME TO PRODUCTIONOperate efficiently
Investigate pipelines with their dependencies, runtime history and diagnostic evidence.
VALUE / PLATFORM LEVERAGEControl risk
Keep recovery, access, audit, and observability inside the workflow.
VALUE / PRODUCTION CONFIDENCEStart with the gaps in your lakehouse workflow.
We’ll map them to platform responsibilities, deployment choices, and a practical evaluation.